Systems and methods are provided herein for recommending a pause position during a binge-watching session. A series containing multiple sequential episodes is provided to a user device. Then it is determined whether the user is engaged in binge-watching the series. If the user is binge-watching the series, a binge compulsion score is determined for each episode of the plurality of sequential episodes, where the binge compulsion score is based on how many additional sequential episodes an average user has watched after watching that episode. Then, in response to determining that the binge compulsion score of the next episode corresponds to a threshold, a prompt is generated for display, where the prompt includes a recommendation to the user to pause the series before the next episode begins.
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2. The method of claim 1, wherein the calculating the continual-consumption value for the episode comprises analyzing historic patterns of continual-consumption behavior subsequent to consumption of the episode to calculate an average rate of consumption.
This invention relates to analyzing media consumption behavior, specifically predicting future consumption patterns based on historical data. The method calculates a continual-consumption value for a media episode by examining past user behavior after the episode was consumed. This involves identifying patterns where users continued consuming additional content after the initial episode and determining an average rate of this subsequent consumption. The technique helps assess how likely users are to engage with further content following an initial media selection, enabling better recommendations or content sequencing. The method may also involve tracking user interactions with media content, such as playback duration, skipping behavior, or navigation patterns, to refine the consumption analysis. By leveraging historical data, the system can predict whether users will continue consuming related or recommended content, improving personalization and engagement metrics. The approach is particularly useful for streaming services, video platforms, or any system where understanding user consumption habits enhances content delivery.
3. The method of claim 2, wherein the analyzing historic patterns of continual-consumption behavior comprises determining for a plurality of user devices a respective rate of consumption of the subsequent episodes of the plurality of content episodes of the series.
This invention relates to analyzing user behavior for content consumption, specifically tracking how users engage with episodic content such as TV series. The problem addressed is the need to predict user engagement with future episodes based on historical consumption patterns. The method involves monitoring a plurality of user devices to determine how quickly users consume subsequent episodes of a series. By analyzing the rate of consumption across multiple devices, the system identifies trends in user behavior, such as whether users binge-watch or consume episodes at a slower pace. This data helps content providers optimize content delivery, recommendations, and marketing strategies. The analysis may also account for factors like user preferences, device type, and time of consumption to refine predictions. The goal is to improve user experience by aligning content distribution with observed consumption habits, reducing drop-off rates, and increasing engagement. The method leverages historical data to dynamically adjust recommendations and notifications, ensuring users receive content at optimal times based on their past behavior. This approach enhances personalization and retention in streaming platforms and other episodic content services.
5. The method of claim 4, wherein the continual-consumption value for the episode is set to be equal to an average of continual-consumption values of the plurality of different content episodes that have a plurality of respective likeness values that exceeds the likeness threshold.
This invention relates to content recommendation systems, specifically methods for determining a continual-consumption value for a media episode based on likeness comparisons with other episodes. The problem addressed is improving content recommendations by dynamically adjusting the continual-consumption value, which measures the likelihood a user will continue watching an episode, using likeness comparisons with similar content. The method involves calculating a likeness value between the episode and multiple other content episodes. If the likeness value exceeds a predefined threshold, the continual-consumption value for the episode is set to the average of the continual-consumption values of those similar episodes. This approach ensures that the recommendation system leverages data from comparable content to refine predictions, enhancing accuracy and personalization. The likeness value is determined by comparing metadata, user engagement patterns, or other relevant attributes between episodes. The continual-consumption value represents the probability that a user will continue watching the episode beyond a certain point, which is critical for optimizing content delivery and user satisfaction. By dynamically adjusting this value based on similar episodes, the system avoids over-reliance on isolated data points and improves recommendation quality. This method is particularly useful in streaming platforms where user retention is a key performance metric.
8. The method of claim 7, wherein the current rate of consumption is based on a dynamic time window.
A system and method for monitoring and managing resource consumption involves tracking the usage of a resource over time and dynamically adjusting consumption rates based on real-time conditions. The method determines a current rate of consumption by analyzing usage data within a dynamic time window, which can vary in length depending on factors such as system load, user behavior, or external conditions. This dynamic window allows for more accurate and responsive adjustments to consumption rates, ensuring efficient resource allocation. The system may also compare the current rate of consumption to a target rate and adjust operations accordingly to maintain optimal performance or prevent resource depletion. The dynamic time window can be recalculated periodically or in response to specific triggers, such as changes in demand or system capacity. This approach improves resource management by adapting to fluctuating conditions, reducing waste, and enhancing system reliability. The method is applicable to various domains, including energy management, data processing, and industrial automation, where precise control of resource consumption is critical.
9. The method of claim 7, wherein the calculating the continual-consumption threshold comprises calculating the continual-consumption threshold based on at least one selected from the group of a user profile, a watching history, and calendar data.
This invention relates to a method for determining a continual-consumption threshold for media content, such as video or audio streams, to optimize user engagement and resource allocation. The problem addressed is the need to dynamically adjust playback conditions based on user behavior and preferences to enhance the viewing experience while efficiently managing system resources. The method involves calculating a continual-consumption threshold, which defines a condition under which media playback continues uninterrupted. This threshold is determined using at least one of a user profile, watching history, or calendar data. The user profile may include preferences, viewing habits, or demographic information. Watching history tracks past media consumption patterns, such as duration, frequency, and type of content consumed. Calendar data may include scheduled events or time-based preferences that influence media consumption behavior. By analyzing these factors, the system can predict when a user is likely to continue watching or listening, allowing for adaptive adjustments in playback quality, buffering strategies, or resource allocation. This ensures a seamless experience while optimizing bandwidth and processing power. The method may also integrate with other techniques, such as real-time monitoring of user interactions, to refine the threshold dynamically. The goal is to balance performance and efficiency based on individual user behavior and contextual data.
11. The system of claim 10, wherein the control circuitry is configured to calculate the continual-consumption value for the episode by analyzing historic patterns of continual-consumption behavior subsequent to consumption of the episode to calculate an average rate of consumption.
This invention relates to a system for analyzing media consumption behavior, specifically tracking how users engage with media content over time. The problem addressed is the need to measure and predict how users continue to consume media after an initial viewing, such as re-watching or revisiting content. The system includes control circuitry that evaluates historic patterns of user behavior following the consumption of a media episode to determine a continual-consumption value. This value represents the average rate at which users return to the content after its initial release or viewing. The system may also identify segments of the media that are most likely to prompt continued engagement, such as cliffhangers or unresolved plot points. Additionally, the system can compare the continual-consumption value of different episodes to assess their long-term appeal and influence content recommendations or production decisions. The analysis helps media providers optimize content delivery and retention strategies by understanding which elements drive sustained user interest.
12. The system of claim 11, wherein the control circuitry is configured to analyze historic patterns of continual-consumption behavior by determining for a plurality of user devices a respective rate of consumption of the subsequent episodes of the plurality of content episodes of the series.
This invention relates to a system for analyzing and managing content consumption patterns, particularly for streaming media services. The system addresses the challenge of predicting user engagement with serialized content, such as TV series or podcasts, by tracking how users consume subsequent episodes after an initial episode. The system includes control circuitry that evaluates historic consumption behavior across multiple user devices to determine the rate at which users continue watching or listening to episodes in a series. This analysis helps identify trends in user engagement, such as drop-off points or binge-watching behavior, enabling content providers to optimize recommendations, marketing strategies, or content delivery. The system may also adjust playback settings or notifications based on these patterns to enhance user experience. By leveraging aggregated consumption data, the system provides insights into audience retention and preferences, allowing for more personalized and efficient content distribution. The invention improves upon traditional recommendation systems by focusing on sequential consumption behavior rather than isolated preferences.
14. The system of claim 13, wherein the continual-consumption value for the episode is set to be equal to an average of continual-consumption values of the plurality of different content episodes that have a plurality of respective likeness values that exceeds the likeness threshold.
This invention relates to a system for determining a continual-consumption value for a content episode, such as a video or audio segment, based on likeness values of related content. The system addresses the challenge of predicting user engagement with content by analyzing similarities between episodes and their consumption patterns. The system calculates a continual-consumption value for an episode by comparing it to other episodes with high likeness values. If an episode's likeness values to multiple other episodes exceed a predefined threshold, its continual-consumption value is set to the average of those episodes' continual-consumption values. This approach ensures that the system dynamically adjusts recommendations based on content similarity and historical engagement data, improving user retention. The system may also include a content database, a likeness calculator, and a consumption analyzer to process and compare episodes. The likeness calculator determines similarity between episodes using features like genre, duration, or user interactions, while the consumption analyzer tracks how users engage with content over time. By leveraging these components, the system provides personalized content recommendations that align with user preferences and behavior.
17. The system of claim 16, wherein the current rate of consumption is based on a dynamic time window.
A system for monitoring and managing resource consumption in real-time applications, particularly in environments where resource allocation must adapt to fluctuating demands. The system addresses the challenge of efficiently distributing limited resources, such as computational power, network bandwidth, or energy, by dynamically adjusting allocation based on real-time usage patterns. The system includes a monitoring module that tracks resource consumption across multiple users or processes, a prediction module that forecasts future demand using historical data and current trends, and an allocation module that redistributes resources to optimize performance and prevent shortages. The system further incorporates a dynamic time window for determining the current rate of consumption, allowing it to adjust its calculations based on varying time intervals. This ensures that resource allocation remains responsive to short-term spikes or long-term trends, improving efficiency and reliability. The system may also include user interfaces for administrators to set thresholds, view consumption analytics, and manually override automatic adjustments when necessary. By dynamically adapting to changing conditions, the system ensures optimal resource utilization while maintaining service quality.
18. The system of claim 16, wherein the control circuitry is configured to calculate the continual-consumption threshold by calculating the continual-consumption threshold based on at least one selected from the group of a user profile, a watching history, and calendar data.
This invention relates to a system for managing media consumption, particularly for determining and applying a continual-consumption threshold to optimize user experience. The system addresses the problem of inefficient media playback control, where users may experience interruptions or delays due to improperly set thresholds for continuous playback. The system includes control circuitry that dynamically adjusts playback parameters to ensure seamless media consumption. A key feature is the ability to calculate the continual-consumption threshold based on user-specific data, such as a user profile, watching history, or calendar data. By analyzing these factors, the system tailors the threshold to individual preferences and habits, improving playback continuity. For example, if a user frequently watches long-form content, the system may set a higher threshold to minimize interruptions. Similarly, calendar data can help predict availability, adjusting thresholds to align with the user's schedule. The system may also incorporate additional data sources to refine threshold calculations, ensuring adaptive and personalized media playback. This approach enhances user satisfaction by reducing disruptions and aligning playback behavior with user patterns.
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July 3, 2019
May 28, 2024
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